{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#  一、案例简介\n",
    "## 1. 案例背景\n",
    ">   随着时代的进步，航空航天作为我国的战略性发展事业，取得了极大成就。航天事业的发展核心是飞行安全，飞行安全既是飞行人员与乘客的生命安全保障，又是航天科技发展的方向和目标。气象条件对飞行的影响是不同的，也是不可避免的，航天部门应对飞行威胁较大的恶劣天气进行分析，采取相应的对策进行防范，不断提高飞行的安全性与可靠性。\n",
    ">   恶劣天气的类型很多，主要包括大风、云、雷电、暴雨、冰雪，另外，大雾、风切变、冰高空急流等也会对飞行安全产生不同程度的影响。恶劣天气的影响具有不确定性，因而应减少在恶劣天气范围内进行飞行。\n",
    ">   通过天气因素上座数量的分析，预测不同天气情况下航班的上座情况，进而调整飞行计划以应对不同天气带来的影响。\n",
    "\n",
    "## 2. 案例意义\n",
    ">   从分析结果将使航空公司依据天气模式调整飞行时间表。它也可以引导乘客做出新的选择。航空公司可以提前几天预测到未来航班上座情况，然后未雨绸缪地进行调整。航空公司也可以预先发出警告更有效分配自己的资源、地勤人员、飞行人员和其他资产以减少损失\n",
    "## 3. 业务图\n",
    "![业务图](image-001.png)\n",
    "## 4. 案例目标\n",
    ">     现在，大多数航班只能对连续进行重复，一般处理天气延误的航班是在其发生之后。我们的大数据能够让他们预先预测延迟，以更好地与乘客沟通，以及优化资源。通过数据分析，预测假设航班和不同天气情况之间的结果，帮助迅速提前发现因天气发生的上座影响。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 二、相关技术\n",
    "- ## 数据采集，通过pymysql连接mysql数据库获取数据 \n",
    "- ## 数据预处理，通过pandas、sklearn进行数据清洗，转换等预处理\n",
    "- ## 数据探索，通过matplotlib及pyecharts进行数据可视化，发现数据规律\n",
    "- ## 数据分析建模，通过机器学习算法库sklearn学习已有数据规律建模，预测指定航班在不同天气下的上座率"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 三、案例步骤"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- ## 1. 数据采集: 从数据库casepro中获取数据表t_plane_order\\t_plane_weather,将表格转存在本地csv并展示数据\n",
    "\n",
    "> 数据库信息： host：10.102.52.248，port=3306， user=\"root\", passwd=\"root\"\n",
    "\n",
    "> 航班订单数据，包含航班时刻表，航班号，子订单，飞行日期，头等舱，公务舱，经济舱，其他，头等舱总数，公务舱总数，经济舱总数，其他总数。\n",
    "\n",
    "> 天气数据，包含基线：日期，高温，低温，天气状况，风，空气"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "           航班时刻表     航班号 子订单        飞行日期  头等舱  公务舱  经济舱   其他 头等舱总数 公务舱总数  \\\n",
      "0            1.0  CA1876      2016-01-03  0.0  0.0  0.0  0.0   0.0   0.0   \n",
      "1            2.0  CA1876      2016-01-04  0.0  0.0  0.0  0.0   0.0   0.0   \n",
      "2            3.0  CA1876      2016-01-08  0.0  0.0  0.0  0.0   0.0   0.0   \n",
      "3            4.0  CA1876      2016-01-10  0.0  0.0  0.0  0.0   0.0   0.0   \n",
      "4            5.0  CA1876      2016-01-11  0.0  0.0  0.0  0.0   0.0   0.0   \n",
      "...          ...     ...  ..         ...  ...  ...  ...  ...   ...   ...   \n",
      "938271  938273.0              2016-12-06  0.0  0.0  0.0  0.0   0.0   0.0   \n",
      "938272  938274.0              2016-12-06  0.0  0.0  0.0  0.0   0.0   0.0   \n",
      "938273  938275.0              2016-12-06  0.0  0.0  0.0  0.0   0.0   0.0   \n",
      "938274  938276.0              2016-12-06  0.0  0.0  0.0  0.0   0.0   0.0   \n",
      "938275  938277.0              2016-12-06  0.0  0.0  0.0  0.0   0.0   0.0   \n",
      "\n",
      "       经济舱总数   其他总数  \n",
      "0        0.0  0.0\\r  \n",
      "1        0.0  0.0\\r  \n",
      "2        0.0  0.0\\r  \n",
      "3        0.0  0.0\\r  \n",
      "4        0.0  0.0\\r  \n",
      "...      ...    ...  \n",
      "938271   0.0  0.0\\r  \n",
      "938272   0.0  0.0\\r  \n",
      "938273   0.0  0.0\\r  \n",
      "938274   0.0  0.0\\r  \n",
      "938275   0.0  0.0\\r  \n",
      "\n",
      "[938276 rows x 12 columns]\n",
      "Wall time: 21.6 s\n"
     ]
    }
   ],
   "source": [
    "# %%time\n",
    "# import pymysql\n",
    "# import pandas as pd\n",
    "# pd.set_option('display.unicode.east_asian_width',True)\n",
    "# conn=pymysql.connect(user='root',host='10.102.52.248',port=3306,password='root',db='casepro',charset='utf8')\n",
    "# cur=conn.cursor()\n",
    "# sql='select * from t_plane_order'\n",
    "# cur.execute(sql)\n",
    "# all_data=cur.fetchall()\n",
    "# plane_order=pd.DataFrame(list(all_data),columns=['航班时刻表','航班号','子订单','飞行日期','头等舱','公务舱','经济舱','其他','头等舱总数','公务舱总数','经济舱总数','其他总数'])\n",
    "# print(plane_order)\n",
    "# plane_order.to_csv('E:/plane_order.csv',header=True,index=False)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Wall time: 56.5 ms\n"
     ]
    }
   ],
   "source": [
    "# %%time\n",
    "# import pymysql\n",
    "# import pandas as pd\n",
    "# conn=pymysql.connect(user='root',host='10.102.52.248',port=3306,password='root',db='casepro',charset='utf8')\n",
    "# cur=conn.cursor()\n",
    "# sql1='select * from t_plane_weather'\n",
    "# cur.execute(sql1)\n",
    "# all_data1=cur.fetchall()\n",
    "# plane_weather=pd.DataFrame(list(all_data1),columns=['日期','高温','低温','天气状况','风','空气'])\n",
    "# plane_weather.to_csv('E:/plane_weather.csv',header=True,index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "成功\n"
     ]
    }
   ],
   "source": [
    "import pymysql #导入模块\n",
    "import pandas as pd\n",
    "\n",
    "#远程连接数据库\n",
    "db = pymysql.connect(\n",
    "         host='10.102.52.248',\n",
    "         port=3306,\n",
    "         user='root',\n",
    "         passwd='root',\n",
    "         db='casepro',\n",
    "         charset='utf8'\n",
    "         )\n",
    "def link_mysql(db):\n",
    "    #使用cursor()方法获取操作游标 \n",
    "    cursor = db.cursor()\n",
    "    sql = \"\"\"SELECT * FROM `t_plane_order`\"\"\"\n",
    "    sql1 = \"\"\"SELECT * FROM `t_plane_weather`\"\"\"\n",
    "    try:\n",
    "        cursor.execute(sql)  # 执行sql语句\n",
    "        result = cursor.fetchall()\n",
    "        columns = ['航班时刻表','航班号','子订单','日期','头等舱','公务舱','经济舱','其他','头等舱总数','公务舱总数','经济舱总数','其他总数']\n",
    "        frame = pd.DataFrame(list(result),columns=columns)\n",
    "        frame.to_csv(\"航班天气因素上座情况预测分析案例.csv\",encoding='utf-8')\n",
    "        cursor.execute(sql1)  # 执行sql语句\n",
    "        result1 = cursor.fetchall()\n",
    "        columns_weather = ['日期','高温','低温','天气状况','风','空气']\n",
    "        frame_weather = pd.DataFrame(list(result1),columns=columns_weather)\n",
    "        frame_weather.to_csv(\"天气数据.csv\",encoding='utf-8')\n",
    "        print(\"成功\")\n",
    "    except Exception:\n",
    "        db.rollback()  # 发生错误时回滚\n",
    "        print(\"查询失败\")\n",
    "    cursor.close()  # 关闭游标\n",
    "    db.close()  # 关闭数据库连接\n",
    "link_mysql(db)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- ## 2、数据预处理（清洗、数值化转换、标准化等）\n",
    "\n",
    "> 空数据判断和处理（注意分析空占比，区分数值型和类别型）\n",
    "\n",
    "> 数据规范性检查（格式、范围等）\n",
    "\n",
    "> 去除不规范数据、无效、无分析价值数据等"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\python\\python1\\lib\\site-packages\\IPython\\core\\interactiveshell.py:3058: DtypeWarning: Columns (2,4) have mixed types. Specify dtype option on import or set low_memory=False.\n",
      "  interactivity=interactivity, compiler=compiler, result=result)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>航班时刻表</th>\n",
       "      <th>航班号</th>\n",
       "      <th>子订单</th>\n",
       "      <th>飞行日期</th>\n",
       "      <th>头等舱</th>\n",
       "      <th>公务舱</th>\n",
       "      <th>经济舱</th>\n",
       "      <th>其他</th>\n",
       "      <th>头等舱总数</th>\n",
       "      <th>公务舱总数</th>\n",
       "      <th>经济舱总数</th>\n",
       "      <th>其他总数</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>52</td>\n",
       "      <td>53.0</td>\n",
       "      <td>CA1876</td>\n",
       "      <td>1</td>\n",
       "      <td>2016-05-03</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>69</td>\n",
       "      <td>70.0</td>\n",
       "      <td>CA1876</td>\n",
       "      <td>1</td>\n",
       "      <td>2016-06-09</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>70</td>\n",
       "      <td>71.0</td>\n",
       "      <td>CA1876</td>\n",
       "      <td>2</td>\n",
       "      <td>2016-06-09</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>73</td>\n",
       "      <td>74.0</td>\n",
       "      <td>CA1876</td>\n",
       "      <td>1</td>\n",
       "      <td>2016-06-13</td>\n",
       "      <td>-2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>159.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>74</td>\n",
       "      <td>75.0</td>\n",
       "      <td>CA1876</td>\n",
       "      <td>2</td>\n",
       "      <td>2016-06-13</td>\n",
       "      <td>-1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-6.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>159.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>926459</td>\n",
       "      <td>926460.0</td>\n",
       "      <td>S7528</td>\n",
       "      <td>1</td>\n",
       "      <td>2016-11-02</td>\n",
       "      <td>0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>926463</td>\n",
       "      <td>926464.0</td>\n",
       "      <td>S7528</td>\n",
       "      <td>1</td>\n",
       "      <td>2016-11-23</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>926467</td>\n",
       "      <td>926468.0</td>\n",
       "      <td>S7528</td>\n",
       "      <td>1</td>\n",
       "      <td>2016-12-14</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>926471</td>\n",
       "      <td>926472.0</td>\n",
       "      <td>Y7888</td>\n",
       "      <td>1</td>\n",
       "      <td>2016-07-14</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>926472</td>\n",
       "      <td>926473.0</td>\n",
       "      <td>Y7888</td>\n",
       "      <td>2</td>\n",
       "      <td>2016-07-14</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>681715 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        航班时刻表  航班号 子订单    飞行日期 头等舱  ...  其他  头等舱总数  公务舱总数  经济舱总数  其他总数\n",
       "52            53.0  CA1876      1  2016-05-03      0  ...   0.0         0.0         0.0         0.0       0.0\n",
       "69            70.0  CA1876      1  2016-06-09      0  ...   0.0         0.0         0.0         0.0       0.0\n",
       "70            71.0  CA1876      2  2016-06-09      0  ...   0.0         0.0         0.0         0.0       0.0\n",
       "73            74.0  CA1876      1  2016-06-13     -2  ...   0.0         8.0         0.0       159.0       0.0\n",
       "74            75.0  CA1876      2  2016-06-13     -1  ...   0.0         8.0         0.0       159.0       0.0\n",
       "...            ...     ...    ...         ...    ...  ...   ...         ...         ...         ...       ...\n",
       "926459    926460.0   S7528      1  2016-11-02      0  ...   0.0         0.0         0.0         0.0       0.0\n",
       "926463    926464.0   S7528      1  2016-11-23      0  ...   0.0         0.0         0.0         0.0       0.0\n",
       "926467    926468.0   S7528      1  2016-12-14      0  ...   0.0         0.0         0.0         0.0       0.0\n",
       "926471    926472.0   Y7888      1  2016-07-14      0  ...   0.0         0.0         0.0         0.0       0.0\n",
       "926472    926473.0   Y7888      2  2016-07-14      0  ...   0.0         0.0         0.0         0.0       0.0\n",
       "\n",
       "[681715 rows x 12 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# import pandas as pd\n",
    "# pd.set_option('display.unicode.east_asian_width',True)\n",
    "# data=pd.read_csv('E:/plane_order.csv',encoding='utf-8')\n",
    "# # print(data.isnull().sum()) #统计每个字段缺失值的数量\n",
    "# # df=data.dropna() #删除缺失值\n",
    "# # print(df)\n",
    "# # df1=df.reset_index(drop=True,inplace=True)\n",
    "# # print(df1)\n",
    "# # print(data[data.duplicated()].index) #重复行索引\n",
    "# df=data.drop_duplicates() #删除重复记录\n",
    "# # df.isnull().sum()\n",
    "# df=df.dropna()\n",
    "# df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{1.0: Int64Index([    52,     69,     73,    168,    265,    267,    271,    467,\n",
       "                484,    507,\n",
       "             ...\n",
       "             926423, 926431, 926434, 926440, 926442, 926451, 926459, 926463,\n",
       "             926467, 926471],\n",
       "            dtype='int64', length=170849),\n",
       " 2.0: Int64Index([    70,     74,    268,   1091,   1101,   1350,   2259,   2265,\n",
       "               2639,   2745,\n",
       "             ...\n",
       "             926220, 926300, 926315, 926318, 926405, 926410, 926415, 926424,\n",
       "             926443, 926472],\n",
       "            dtype='int64', length=145989),\n",
       " 3.0: Int64Index([  1092,   1102,   1351,   3083,   4816,   4948,   4996,   5414,\n",
       "               6533,   6552,\n",
       "             ...\n",
       "             925718, 925749, 925761, 925772, 925784, 925788, 925803, 925889,\n",
       "             926108, 926444],\n",
       "            dtype='int64', length=116143),\n",
       " 4.0: Int64Index([  1093,   5415,   6534,   6553,   6633,   7228,   7235,   8511,\n",
       "               8626,   8650,\n",
       "             ...\n",
       "             925379, 925389, 925394, 925460, 925603, 925635, 925651, 925698,\n",
       "             925762, 925804],\n",
       "            dtype='int64', length=76956),\n",
       " 5.0: Int64Index([  5416,   6535,   6554,   6634,   7229,   7236,   8627,   8651,\n",
       "               8657,  11772,\n",
       "             ...\n",
       "             924297, 924371, 924437, 924562, 924618, 925170, 925197, 925231,\n",
       "             925304, 925395],\n",
       "            dtype='int64', length=42734),\n",
       " 6.0: Int64Index([  5417,   6536,   6555,   6635,   7230,   7237,   8658,  11785,\n",
       "              12879,  13901,\n",
       "             ...\n",
       "             923864, 923893, 923959, 924030, 924132, 924208, 924298, 924563,\n",
       "             925171, 925198],\n",
       "            dtype='int64', length=20815),\n",
       " 7.0: Int64Index([  5418,   6636,   7238,   8659,  11786,  12880,  13902,  14459,\n",
       "              21225,  21341,\n",
       "             ...\n",
       "             923244, 923515, 923840, 923865, 923894, 924031, 924209, 924299,\n",
       "             925172, 925199],\n",
       "            dtype='int64', length=9273),\n",
       " 8.0: Int64Index([  7239,  21226,  28699,  34238,  36453,  41381,  47197,  47234,\n",
       "              47305,  47892,\n",
       "             ...\n",
       "             783888, 784445, 784514, 784694, 784962, 785842, 786144, 923245,\n",
       "             923516, 924210],\n",
       "            dtype='int64', length=3929),\n",
       " 9.0: Int64Index([ 21227,  34239,  36454,  47235,  49444,  49517,  50658,  51372,\n",
       "              51515,  53288,\n",
       "             ...\n",
       "             779688, 780149, 780705, 780763, 781163, 781440, 782271, 782674,\n",
       "             783569, 784963],\n",
       "            dtype='int64', length=1687),\n",
       " 10.0: Int64Index([ 21218,  34231,  49509,  51364,  53278,  53964,  55210,  55723,\n",
       "              55756,  56165,\n",
       "             ...\n",
       "             760368, 762311, 764320, 771658, 774547, 777579, 779680, 781155,\n",
       "             781431, 784955],\n",
       "            dtype='int64', length=718),\n",
       " 11.0: Int64Index([ 21219,  53279,  55211,  56166,  57024,  57526,  59090,  59991,\n",
       "              61820,  63536,\n",
       "             ...\n",
       "             745606, 747202, 747256, 748450, 759174, 762312, 764321, 771659,\n",
       "             777580, 781432],\n",
       "            dtype='int64', length=303),\n",
       " 12.0: Int64Index([ 53280,  56167,  57025,  89861,  91699,  94755,  99095, 105166,\n",
       "             114540, 114835,\n",
       "             ...\n",
       "             741148, 741199, 741527, 741674, 741798, 742084, 742115, 742244,\n",
       "             743950, 747257],\n",
       "            dtype='int64', length=133),\n",
       " 13.0: Int64Index([ 57026,  91700, 114541, 115491, 115555, 117461, 163846, 177903,\n",
       "             181119, 186385, 206827, 245880, 249787, 254290, 265139, 269159,\n",
       "             282772, 306007, 314682, 348756, 349010, 349025, 354863, 379708,\n",
       "             380523, 382080, 383091, 385429, 385543, 386329, 388910, 391859,\n",
       "             394252, 426007, 441989, 442160, 493227, 530944, 601106, 608647,\n",
       "             610082, 683200, 685134, 686253, 689183, 693542, 700678, 701994,\n",
       "             703600, 703782, 706613, 706819, 706854, 708383, 722732, 731402,\n",
       "             731823, 740740, 740823, 741675, 741799, 742085],\n",
       "            dtype='int64'),\n",
       " 14.0: Int64Index([ 57027, 114542, 115492, 115556, 117462, 245881, 249788, 254291,\n",
       "             282773, 306008, 348757, 349011, 349026, 354864, 380524, 385430,\n",
       "             385544, 394253, 441990, 686254, 701995, 706614, 706820, 706855,\n",
       "             708384, 722733, 731824, 740741, 741676, 741800, 742086],\n",
       "            dtype='int64'),\n",
       " 15.0: Int64Index([ 57028, 245882, 249789, 254292, 306009, 349027, 380525, 385545,\n",
       "             686255, 706615, 706821, 706856, 708385, 731825, 740742, 742087],\n",
       "            dtype='int64'),\n",
       " 16.0: Int64Index([254293, 306010, 380526, 385546, 706857, 708386, 731826], dtype='int64'),\n",
       " 17.0: Int64Index([254294, 385547, 708387, 731827], dtype='int64'),\n",
       " 18.0: Int64Index([254295, 731828], dtype='int64'),\n",
       " 19.0: Int64Index([731829], dtype='int64'),\n",
       " 20.0: Int64Index([731831], dtype='int64'),\n",
       " '1': Int64Index([786435, 786439, 786444, 786449, 786454, 786457, 786461, 786467,\n",
       "             786476, 786481,\n",
       "             ...\n",
       "             917470, 917473, 917477, 917480, 917483, 917486, 917489, 917494,\n",
       "             917498, 917502],\n",
       "            dtype='int64', length=33909),\n",
       " '10': Int64Index([789781, 792320, 792682, 797896, 799145, 804338, 804559, 805001,\n",
       "             805447, 812205, 812827, 813373, 816151, 817699, 817871, 818057,\n",
       "             819566, 819677, 819771, 819869, 826404, 827035, 827114, 828345,\n",
       "             828589, 830555, 831039, 831101, 831178, 831254, 831303, 831496,\n",
       "             831663, 831938, 831969, 833723, 837532, 837567, 838155, 855855,\n",
       "             857067, 862869, 862936, 864070, 865255, 869261, 871216, 872337,\n",
       "             874818, 875581, 875802, 876845, 877236, 889633, 902960, 912920],\n",
       "            dtype='int64'),\n",
       " '11': Int64Index([792321, 792683, 804560, 805002, 805448, 812206, 812828, 813374,\n",
       "             817872, 819567, 826405, 827036, 828346, 830556, 831102, 831255,\n",
       "             831304, 831939, 833724, 837533, 837568, 862937, 864071, 865256,\n",
       "             874819, 875803, 889634, 902961],\n",
       "            dtype='int64'),\n",
       " '12': Int64Index([805003, 805449, 817873, 826406, 827037, 828347, 830557, 831305,\n",
       "             831940, 837534, 837569, 862938, 865257, 902962],\n",
       "            dtype='int64'),\n",
       " '13': Int64Index([805004, 831306, 837535, 837570, 865258], dtype='int64'),\n",
       " '14': Int64Index([805005, 837536, 837571], dtype='int64'),\n",
       " '15': Int64Index([805006, 837537, 837572], dtype='int64'),\n",
       " '16': Int64Index([805007, 837538, 837573], dtype='int64'),\n",
       " '17': Int64Index([837539, 837574], dtype='int64'),\n",
       " '18': Int64Index([837575], dtype='int64'),\n",
       " '19': Int64Index([837576], dtype='int64'),\n",
       " '2': Int64Index([786432, 786436, 786440, 786445, 786450, 786455, 786458, 786462,\n",
       "             786468, 786477,\n",
       "             ...\n",
       "             917471, 917474, 917478, 917481, 917484, 917487, 917490, 917495,\n",
       "             917499, 917503],\n",
       "            dtype='int64', length=22373),\n",
       " '20': Int64Index([837578], dtype='int64'),\n",
       " '2016-': Int64Index([849187, 849188, 849189, 849190, 849191, 849192, 849193, 849194,\n",
       "             849195, 849196,\n",
       "             ...\n",
       "             852802, 852803, 852804, 852805, 852806, 852807, 852808, 852809,\n",
       "             852810, 852811],\n",
       "            dtype='int64', length=1831),\n",
       " '21': Int64Index([837579], dtype='int64'),\n",
       " '22': Int64Index([837580], dtype='int64'),\n",
       " '23': Int64Index([837581], dtype='int64'),\n",
       " '3': Int64Index([786433, 786437, 786441, 786446, 786451, 786459, 786463, 786469,\n",
       "             786478, 786483,\n",
       "             ...\n",
       "             917421, 917427, 917434, 917438, 917443, 917450, 917475, 917491,\n",
       "             917496, 917500],\n",
       "            dtype='int64', length=15802),\n",
       " '4': Int64Index([786442, 786447, 786452, 786464, 786470, 786479, 786488, 786494,\n",
       "             786502, 786516,\n",
       "             ...\n",
       "             917213, 917276, 917296, 917323, 917344, 917355, 917417, 917422,\n",
       "             917439, 917492],\n",
       "            dtype='int64', length=9749),\n",
       " '5': Int64Index([786465, 786471, 786489, 786495, 786503, 786517, 786561, 786586,\n",
       "             786600, 786612,\n",
       "             ...\n",
       "             916668, 916721, 916733, 916817, 916861, 917126, 917134, 917214,\n",
       "             917277, 917423],\n",
       "            dtype='int64', length=4894),\n",
       " '6': Int64Index([786472, 786496, 786629, 786824, 786899, 786949, 787080, 787181,\n",
       "             787242, 787427,\n",
       "             ...\n",
       "             914019, 914394, 914494, 916148, 916495, 916586, 916624, 916818,\n",
       "             916862, 917278],\n",
       "            dtype='int64', length=2103),\n",
       " '7': Int64Index([786473, 786497, 786900, 787081, 787479, 787508, 787683, 787757,\n",
       "             787937, 788086,\n",
       "             ...\n",
       "             912048, 912058, 912696, 912926, 913000, 913332, 914020, 916149,\n",
       "             916587, 916863],\n",
       "            dtype='int64', length=826),\n",
       " '8': Int64Index([786474, 786901, 787480, 787758, 787938, 788087, 788190, 788211,\n",
       "             789535, 789788,\n",
       "             ...\n",
       "             903033, 903549, 903745, 904224, 907933, 910593, 910747, 912059,\n",
       "             912927, 916588],\n",
       "            dtype='int64', length=325),\n",
       " '9': Int64Index([786902, 788088, 789789, 791776, 792329, 792691, 797364, 797904,\n",
       "             798013, 798091,\n",
       "             ...\n",
       "             878125, 878858, 889642, 902970, 903034, 903550, 904225, 910594,\n",
       "             912928, 916589],\n",
       "            dtype='int64', length=130)}"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# import pandas as pd\n",
    "# pd.set_option('display.unicode.east_asian_width',True)\n",
    "# data=pd.read_csv('E:/plane_order.csv')\n",
    "# df=data.drop_duplicates() #删除重复记录\n",
    "# # print(df.isnull().sum()) #缺失值的统计\n",
    "# df=df.dropna()\n",
    "# df=df.groupby('子订单').groups\n",
    "# df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Wall time: 0 ns\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "#对航班数据进行清理\n",
    "def yucli_plane():\n",
    "    df_plane = pd.read_csv('航班天气因素上座情况预测分析案例.csv',encoding='utf-8')\n",
    "    df_plane = pd.DataFrame(data=df_plane)\n",
    "    df_plane = df_plane.drop(columns='Unnamed: 0')\n",
    "    #判断其空占比\n",
    "    df_plane_percent = df_plane.isna().sum().sort_values(ascending=False) / len(df_plane)\n",
    "    print(\"各字段的空占比为：\\n{}\".format(df_plane_percent))\n",
    "    #删除空数据\n",
    "    df_plane_notNu = df_plane.dropna(axis=0)\n",
    "    #删除重复数据\n",
    "    df_plane_notNu = df_plane_notNu.drop_duplicates()\n",
    "    #将str类型改为float\n",
    "    for i in range(4,12):\n",
    "        df_plane_notNu.iloc[:,i] = df_plane_notNu.iloc[:,i].astype(float)\n",
    "    #将负值替换成零\n",
    "    for i in range(4,12):\n",
    "        df_plane_notNu.iloc[:,i][df_plane_notNu.iloc[:,i] < 0] = 0.0\n",
    "    #删除日期中不规范的数据\n",
    "    df_plane_notNu[df_plane_notNu.iloc[:,3] == \"0.0\"] = np.nan\n",
    "    df_plane_notNu.dropna(axis=0)\n",
    "    return df_plane_notNu\n",
    "\n",
    "#对天气数据进行处理\n",
    "def yucli_weath():\n",
    "    df_weath = pd.read_csv('天气数据.csv',encoding='utf-8')\n",
    "    df_weath = pd.DataFrame(df_weath)\n",
    "    #删除无分析价值数据\n",
    "    df_weath = df_weath.drop('Unnamed: 0',axis=1)\n",
    "    #将日期和空气格式化\n",
    "    df_weath['日期'] = df_weath['日期'].apply(lambda x:str(x)[0:10])\n",
    "    df_weath['空气'] = df_weath['空气'].apply(lambda x:str(x)[:-1])\n",
    "    return df_weath"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\python\\python1\\lib\\site-packages\\IPython\\core\\interactiveshell.py:3249: DtypeWarning: Columns (3,5) have mixed types. Specify dtype option on import or set low_memory=False.\n",
      "  if (await self.run_code(code, result,  async_=asy)):\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "各字段的空占比为：\n",
      "子订单      0.272959\n",
      "航班号      0.013529\n",
      "日期       0.000389\n",
      "其他总数     0.000000\n",
      "经济舱总数    0.000000\n",
      "公务舱总数    0.000000\n",
      "头等舱总数    0.000000\n",
      "其他       0.000000\n",
      "经济舱      0.000000\n",
      "公务舱      0.000000\n",
      "头等舱      0.000000\n",
      "航班时刻表    0.000000\n",
      "dtype: float64\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\python\\python1\\lib\\site-packages\\ipykernel_launcher.py:21: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>航班时刻表</th>\n",
       "      <th>航班号</th>\n",
       "      <th>子订单</th>\n",
       "      <th>日期</th>\n",
       "      <th>头等舱</th>\n",
       "      <th>公务舱</th>\n",
       "      <th>经济舱</th>\n",
       "      <th>其他</th>\n",
       "      <th>头等舱总数</th>\n",
       "      <th>公务舱总数</th>\n",
       "      <th>经济舱总数</th>\n",
       "      <th>其他总数</th>\n",
       "      <th>高温</th>\n",
       "      <th>低温</th>\n",
       "      <th>天气状况</th>\n",
       "      <th>风</th>\n",
       "      <th>空气</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>53.0</td>\n",
       "      <td>CA1876</td>\n",
       "      <td>1</td>\n",
       "      <td>2016-05-03</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>多云</td>\n",
       "      <td>北风微风</td>\n",
       "      <td>优</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>2265.0</td>\n",
       "      <td>CA1626</td>\n",
       "      <td>1</td>\n",
       "      <td>2016-05-03</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>多云</td>\n",
       "      <td>北风微风</td>\n",
       "      <td>优</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>2266.0</td>\n",
       "      <td>CA1626</td>\n",
       "      <td>2</td>\n",
       "      <td>2016-05-03</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>147.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>多云</td>\n",
       "      <td>北风微风</td>\n",
       "      <td>优</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>5208.0</td>\n",
       "      <td>CA1143</td>\n",
       "      <td>1</td>\n",
       "      <td>2016-05-03</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>多云</td>\n",
       "      <td>北风微风</td>\n",
       "      <td>优</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>6337.0</td>\n",
       "      <td>CA711</td>\n",
       "      <td>1</td>\n",
       "      <td>2016-05-03</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>多云</td>\n",
       "      <td>北风微风</td>\n",
       "      <td>优</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>679879</td>\n",
       "      <td>924324.0</td>\n",
       "      <td>EK307</td>\n",
       "      <td>2</td>\n",
       "      <td>2016-10-29</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>76.0</td>\n",
       "      <td>427.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>多云~阴</td>\n",
       "      <td>无持续风向微风</td>\n",
       "      <td>良</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>679880</td>\n",
       "      <td>925583.0</td>\n",
       "      <td>EK309</td>\n",
       "      <td>1</td>\n",
       "      <td>2016-10-29</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>42.0</td>\n",
       "      <td>310.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>多云~阴</td>\n",
       "      <td>无持续风向微风</td>\n",
       "      <td>良</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>679881</td>\n",
       "      <td>925584.0</td>\n",
       "      <td>EK309</td>\n",
       "      <td>2</td>\n",
       "      <td>2016-10-29</td>\n",
       "      <td>1.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>42.0</td>\n",
       "      <td>310.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>多云~阴</td>\n",
       "      <td>无持续风向微风</td>\n",
       "      <td>良</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>679882</td>\n",
       "      <td>926220.0</td>\n",
       "      <td>LD769</td>\n",
       "      <td>1</td>\n",
       "      <td>2016-10-29</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>多云~阴</td>\n",
       "      <td>无持续风向微风</td>\n",
       "      <td>良</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>679883</td>\n",
       "      <td>926221.0</td>\n",
       "      <td>LD769</td>\n",
       "      <td>2</td>\n",
       "      <td>2016-10-29</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>多云~阴</td>\n",
       "      <td>无持续风向微风</td>\n",
       "      <td>良</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>679884 rows × 17 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           航班时刻表     航班号 子订单          日期  头等舱  公务舱  经济舱   其他  头等舱总数  公务舱总数  \\\n",
       "0           53.0  CA1876   1  2016-05-03  0.0  0.0  0.0  0.0    0.0    0.0   \n",
       "1         2265.0  CA1626   1  2016-05-03  0.0  0.0  0.0  0.0    0.0    0.0   \n",
       "2         2266.0  CA1626   2  2016-05-03  0.0  0.0  0.0  0.0   12.0    0.0   \n",
       "3         5208.0  CA1143   1  2016-05-03  0.0  0.0  0.0  0.0    0.0    0.0   \n",
       "4         6337.0   CA711   1  2016-05-03  0.0  0.0  0.0  0.0    0.0    0.0   \n",
       "...          ...     ...  ..         ...  ...  ...  ...  ...    ...    ...   \n",
       "679879  924324.0   EK307   2  2016-10-29  1.0  0.0  8.0  0.0   14.0   76.0   \n",
       "679880  925583.0   EK309   1  2016-10-29  0.0  0.0  0.0  0.0    8.0   42.0   \n",
       "679881  925584.0   EK309   2  2016-10-29  1.0  6.0  5.0  0.0    8.0   42.0   \n",
       "679882  926220.0   LD769   1  2016-10-29  0.0  0.0  0.0  0.0    0.0    0.0   \n",
       "679883  926221.0   LD769   2  2016-10-29  0.0  0.0  0.0  0.0    0.0    0.0   \n",
       "\n",
       "        经济舱总数  其他总数    高温   低温  天气状况        风 空气  \n",
       "0         0.0   0.0  28.0  8.0    多云     北风微风  优  \n",
       "1         0.0   0.0  28.0  8.0    多云     北风微风  优  \n",
       "2       147.0   0.0  28.0  8.0    多云     北风微风  优  \n",
       "3         0.0   0.0  28.0  8.0    多云     北风微风  优  \n",
       "4         0.0   0.0  28.0  8.0    多云     北风微风  优  \n",
       "...       ...   ...   ...  ...   ...      ... ..  \n",
       "679879  427.0   0.0  11.0  2.0  多云~阴  无持续风向微风  良  \n",
       "679880  310.0   0.0  11.0  2.0  多云~阴  无持续风向微风  良  \n",
       "679881  310.0   0.0  11.0  2.0  多云~阴  无持续风向微风  良  \n",
       "679882    0.0   0.0  11.0  2.0  多云~阴  无持续风向微风  良  \n",
       "679883    0.0   0.0  11.0  2.0  多云~阴  无持续风向微风  良  \n",
       "\n",
       "[679884 rows x 17 columns]"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_result = pd.merge(left=yucli_plane(), right=yucli_weath(), on=\"日期\", how=\"inner\")\n",
    "df_result.groupby(['日期','航班号','天气状况','风','空气']).sum()\n",
    "df_result"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- ## 3、数据探索，发现数据中的规律"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 2000x800 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "plt.rcParams['font.sans-serif']=['Simhei']\n",
    "plt.rcParams['axes.unicode_minus']=False\n",
    "df=df_result.groupby('高温',as_index=False).sum()\n",
    "x=df['高温']\n",
    "y=df['头等舱']\n",
    "y1=df['公务舱']\n",
    "y2=df['经济舱']\n",
    "width=0.3\n",
    "plt.figure(figsize=(20, 8), dpi=100)\n",
    "plt.xticks(np.arange(-15,40))\n",
    "plt.xlabel('高温温度(摄氏度)')\n",
    "plt.ylabel('头等舱数量')\n",
    "plt.title('高温对各舱数量影响分析图')\n",
    "plt.bar(x,y,width=width,color='green') #紫罗兰颜色\n",
    "plt.bar(x+width,y1,width=width,color='blue') #海军蓝颜色\n",
    "plt.bar(x+2*width,y2,width=width,color='red') #浅海蓝绿\n",
    "plt.legend(['头等舱','公务舱','经济舱'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "<script>\n",
       "    require.config({\n",
       "        paths: {\n",
       "            'echarts':'http://localhost:8888/nbextensions/assets/echarts.min'\n",
       "        }\n",
       "    });\n",
       "</script>\n",
       "\n",
       "        <div id=\"0bb8fcf59de347d192e53aa45d3a3a96\" style=\"width:1000px; height:1000px;\"></div>\n",
       "\n",
       "<script>\n",
       "        require(['echarts'], function(echarts) {\n",
       "                var chart_0bb8fcf59de347d192e53aa45d3a3a96 = echarts.init(\n",
       "                    document.getElementById('0bb8fcf59de347d192e53aa45d3a3a96'), 'white', {renderer: 'canvas'});\n",
       "                var option_0bb8fcf59de347d192e53aa45d3a3a96 = {\n",
       "    \"backgroundColor\": \"white\",\n",
       "    \"animation\": true,\n",
       "    \"animationThreshold\": 2000,\n",
       "    \"animationDuration\": 1000,\n",
       "    \"animationEasing\": \"cubicOut\",\n",
       "    \"animationDelay\": 0,\n",
       "    \"animationDurationUpdate\": 300,\n",
       "    \"animationEasingUpdate\": \"cubicOut\",\n",
       "    \"animationDelayUpdate\": 0,\n",
       "    \"color\": [\n",
       "        \"#c23531\",\n",
       "        \"#2f4554\",\n",
       "        \"#61a0a8\",\n",
       "        \"#d48265\",\n",
       "        \"#749f83\",\n",
       "        \"#ca8622\",\n",
       "        \"#bda29a\",\n",
       "        \"#6e7074\",\n",
       "        \"#546570\",\n",
       "        \"#c4ccd3\",\n",
       "        \"#f05b72\",\n",
       "        \"#ef5b9c\",\n",
       "        \"#f47920\",\n",
       "        \"#905a3d\",\n",
       "        \"#fab27b\",\n",
       "        \"#2a5caa\",\n",
       "        \"#444693\",\n",
       "        \"#726930\",\n",
       "        \"#b2d235\",\n",
       "        \"#6d8346\",\n",
       "        \"#ac6767\",\n",
       "        \"#1d953f\",\n",
       "        \"#6950a1\",\n",
       "        \"#918597\"\n",
       "    ],\n",
       "    \"series\": [\n",
       "        {\n",
       "            \"type\": \"bar\",\n",
       "            \"name\": \"\\u5934\\u7b49\\u8231\",\n",
       "            \"legendHoverLink\": true,\n",
       "            \"data\": [\n",
       "                790.0,\n",
       "                1697.0,\n",
       "                1674.0,\n",
       "                1799.0,\n",
       "                3767.0,\n",
       "                1515.0,\n",
       "                4563.0,\n",
       "                4687.0,\n",
       "                4689.0,\n",
       "                6798.0,\n",
       "                7826.0,\n",
       "                10881.0,\n",
       "                7379.0,\n",
       "                8162.0,\n",
       "                6573.0,\n",
       "                5998.0,\n",
       "                8483.0,\n",
       "                4556.0,\n",
       "                9280.0,\n",
       "                6184.0,\n",
       "                4010.0,\n",
       "                2975.0,\n",
       "                6058.0,\n",
       "                4081.0,\n",
       "                5886.0,\n",
       "                7377.0,\n",
       "                3210.0,\n",
       "                9304.0,\n",
       "                5905.0,\n",
       "                6518.0,\n",
       "                7523.0,\n",
       "                9004.0,\n",
       "                9156.0,\n",
       "                7066.0,\n",
       "                10401.0,\n",
       "                7955.0,\n",
       "                8687.0,\n",
       "                18204.0,\n",
       "                19845.0,\n",
       "                17864.0,\n",
       "                16402.0,\n",
       "                11527.0,\n",
       "                10560.0,\n",
       "                1513.0,\n",
       "                4660.0,\n",
       "                3039.0\n",
       "            ],\n",
       "            \"showBackground\": false,\n",
       "            \"barMinHeight\": 0,\n",
       "            \"barCategoryGap\": \"20%\",\n",
       "            \"barGap\": \"30%\",\n",
       "            \"large\": false,\n",
       "            \"largeThreshold\": 400,\n",
       "            \"seriesLayoutBy\": \"column\",\n",
       "            \"datasetIndex\": 0,\n",
       "            \"clip\": true,\n",
       "            \"zlevel\": 0,\n",
       "            \"z\": 2,\n",
       "            \"label\": {\n",
       "                \"show\": true,\n",
       "                \"position\": \"top\",\n",
       "                \"margin\": 8\n",
       "            }\n",
       "        },\n",
       "        {\n",
       "            \"type\": \"bar\",\n",
       "            \"name\": \"\\u516c\\u52a1\\u8231\",\n",
       "            \"legendHoverLink\": true,\n",
       "            \"data\": [\n",
       "                548.0,\n",
       "                1051.0,\n",
       "                1890.0,\n",
       "                1026.0,\n",
       "                1505.0,\n",
       "                862.0,\n",
       "                2979.0,\n",
       "                2806.0,\n",
       "                2308.0,\n",
       "                3949.0,\n",
       "                4311.0,\n",
       "                6203.0,\n",
       "                3309.0,\n",
       "                4318.0,\n",
       "                2952.0,\n",
       "                3351.0,\n",
       "                4903.0,\n",
       "                2333.0,\n",
       "                4612.0,\n",
       "                3576.0,\n",
       "                2037.0,\n",
       "                1332.0,\n",
       "                2851.0,\n",
       "                2073.0,\n",
       "                3100.0,\n",
       "                2721.0,\n",
       "                1283.0,\n",
       "                4804.0,\n",
       "                2686.0,\n",
       "                3286.0,\n",
       "                3264.0,\n",
       "                3922.0,\n",
       "                4199.0,\n",
       "                3490.0,\n",
       "                4738.0,\n",
       "                4584.0,\n",
       "                4159.0,\n",
       "                8644.0,\n",
       "                10556.0,\n",
       "                10145.0,\n",
       "                8614.0,\n",
       "                6805.0,\n",
       "                5918.0,\n",
       "                702.0,\n",
       "                2479.0,\n",
       "                1401.0\n",
       "            ],\n",
       "            \"showBackground\": false,\n",
       "            \"barMinHeight\": 0,\n",
       "            \"barCategoryGap\": \"20%\",\n",
       "            \"barGap\": \"30%\",\n",
       "            \"large\": false,\n",
       "            \"largeThreshold\": 400,\n",
       "            \"seriesLayoutBy\": \"column\",\n",
       "            \"datasetIndex\": 0,\n",
       "            \"clip\": true,\n",
       "            \"zlevel\": 0,\n",
       "            \"z\": 2,\n",
       "            \"label\": {\n",
       "                \"show\": true,\n",
       "                \"position\": \"top\",\n",
       "                \"margin\": 8\n",
       "            }\n",
       "        },\n",
       "        {\n",
       "            \"type\": \"bar\",\n",
       "            \"name\": \"\\u7ecf\\u6d4e\\u8231\",\n",
       "            \"legendHoverLink\": true,\n",
       "            \"data\": [\n",
       "                4888.0,\n",
       "                13698.0,\n",
       "                9565.0,\n",
       "                15662.0,\n",
       "                18048.0,\n",
       "                12874.0,\n",
       "                36227.0,\n",
       "                26167.0,\n",
       "                32565.0,\n",
       "                44190.0,\n",
       "                53157.0,\n",
       "                80639.0,\n",
       "                46515.0,\n",
       "                57054.0,\n",
       "                42822.0,\n",
       "                45533.0,\n",
       "                55410.0,\n",
       "                35211.0,\n",
       "                65679.0,\n",
       "                43017.0,\n",
       "                23258.0,\n",
       "                18781.0,\n",
       "                43234.0,\n",
       "                29407.0,\n",
       "                42641.0,\n",
       "                51398.0,\n",
       "                19034.0,\n",
       "                64681.0,\n",
       "                37620.0,\n",
       "                39159.0,\n",
       "                47388.0,\n",
       "                55201.0,\n",
       "                61593.0,\n",
       "                45214.0,\n",
       "                70315.0,\n",
       "                49375.0,\n",
       "                49450.0,\n",
       "                109824.0,\n",
       "                120800.0,\n",
       "                116956.0,\n",
       "                106625.0,\n",
       "                74309.0,\n",
       "                68741.0,\n",
       "                10167.0,\n",
       "                29064.0,\n",
       "                19904.0\n",
       "            ],\n",
       "            \"showBackground\": false,\n",
       "            \"barMinHeight\": 0,\n",
       "            \"barCategoryGap\": \"20%\",\n",
       "            \"barGap\": \"30%\",\n",
       "            \"large\": false,\n",
       "            \"largeThreshold\": 400,\n",
       "            \"seriesLayoutBy\": \"column\",\n",
       "            \"datasetIndex\": 0,\n",
       "            \"clip\": true,\n",
       "            \"zlevel\": 0,\n",
       "            \"z\": 2,\n",
       "            \"label\": {\n",
       "                \"show\": true,\n",
       "                \"position\": \"top\",\n",
       "                \"margin\": 8\n",
       "            }\n",
       "        }\n",
       "    ],\n",
       "    \"legend\": [\n",
       "        {\n",
       "            \"data\": [\n",
       "                \"\\u5934\\u7b49\\u8231\",\n",
       "                \"\\u516c\\u52a1\\u8231\",\n",
       "                \"\\u7ecf\\u6d4e\\u8231\"\n",
       "            ],\n",
       "            \"selected\": {\n",
       "                \"\\u5934\\u7b49\\u8231\": true,\n",
       "                \"\\u516c\\u52a1\\u8231\": true,\n",
       "                \"\\u7ecf\\u6d4e\\u8231\": true\n",
       "            },\n",
       "            \"show\": true,\n",
       "            \"padding\": 5,\n",
       "            \"itemGap\": 10,\n",
       "            \"itemWidth\": 25,\n",
       "            \"itemHeight\": 14\n",
       "        }\n",
       "    ],\n",
       "    \"tooltip\": {\n",
       "        \"show\": true,\n",
       "        \"trigger\": \"item\",\n",
       "        \"triggerOn\": \"mousemove|click\",\n",
       "        \"axisPointer\": {\n",
       "            \"type\": \"line\"\n",
       "        },\n",
       "        \"showContent\": true,\n",
       "        \"alwaysShowContent\": false,\n",
       "        \"showDelay\": 0,\n",
       "        \"hideDelay\": 100,\n",
       "        \"textStyle\": {\n",
       "            \"fontSize\": 14\n",
       "        },\n",
       "        \"borderWidth\": 0,\n",
       "        \"padding\": 5\n",
       "    },\n",
       "    \"xAxis\": [\n",
       "        {\n",
       "            \"show\": true,\n",
       "            \"scale\": false,\n",
       "            \"nameLocation\": \"end\",\n",
       "            \"nameGap\": 15,\n",
       "            \"gridIndex\": 0,\n",
       "            \"inverse\": false,\n",
       "            \"offset\": 0,\n",
       "            \"splitNumber\": 5,\n",
       "            \"minInterval\": 0,\n",
       "            \"splitLine\": {\n",
       "                \"show\": false,\n",
       "                \"lineStyle\": {\n",
       "                    \"show\": true,\n",
       "                    \"width\": 1,\n",
       "                    \"opacity\": 1,\n",
       "                    \"curveness\": 0,\n",
       "                    \"type\": \"solid\"\n",
       "                }\n",
       "            },\n",
       "            \"data\": [\n",
       "                -14.0,\n",
       "                -6.0,\n",
       "                -5.0,\n",
       "                -4.0,\n",
       "                -3.0,\n",
       "                -2.0,\n",
       "                -1.0,\n",
       "                0.0,\n",
       "                1.0,\n",
       "                2.0,\n",
       "                3.0,\n",
       "                4.0,\n",
       "                5.0,\n",
       "                6.0,\n",
       "                7.0,\n",
       "                8.0,\n",
       "                9.0,\n",
       "                10.0,\n",
       "                11.0,\n",
       "                12.0,\n",
       "                13.0,\n",
       "                14.0,\n",
       "                15.0,\n",
       "                16.0,\n",
       "                17.0,\n",
       "                18.0,\n",
       "                19.0,\n",
       "                20.0,\n",
       "                21.0,\n",
       "                22.0,\n",
       "                23.0,\n",
       "                24.0,\n",
       "                25.0,\n",
       "                26.0,\n",
       "                27.0,\n",
       "                28.0,\n",
       "                29.0,\n",
       "                30.0,\n",
       "                31.0,\n",
       "                32.0,\n",
       "                33.0,\n",
       "                34.0,\n",
       "                35.0,\n",
       "                36.0,\n",
       "                37.0,\n",
       "                38.0\n",
       "            ]\n",
       "        }\n",
       "    ],\n",
       "    \"yAxis\": [\n",
       "        {\n",
       "            \"show\": true,\n",
       "            \"scale\": false,\n",
       "            \"nameLocation\": \"end\",\n",
       "            \"nameGap\": 15,\n",
       "            \"gridIndex\": 0,\n",
       "            \"inverse\": false,\n",
       "            \"offset\": 0,\n",
       "            \"splitNumber\": 5,\n",
       "            \"minInterval\": 0,\n",
       "            \"splitLine\": {\n",
       "                \"show\": false,\n",
       "                \"lineStyle\": {\n",
       "                    \"show\": true,\n",
       "                    \"width\": 1,\n",
       "                    \"opacity\": 1,\n",
       "                    \"curveness\": 0,\n",
       "                    \"type\": \"solid\"\n",
       "                }\n",
       "            }\n",
       "        }\n",
       "    ],\n",
       "    \"title\": [\n",
       "        {\n",
       "            \"text\": \"\\u9ad8\\u6e29\\u5bf9\\u5404\\u8231\\u6570\\u91cf\\u5f71\\u54cd\\u5206\\u6790\\u56fe\",\n",
       "            \"padding\": 5,\n",
       "            \"itemGap\": 10\n",
       "        }\n",
       "    ]\n",
       "};\n",
       "                chart_0bb8fcf59de347d192e53aa45d3a3a96.setOption(option_0bb8fcf59de347d192e53aa45d3a3a96);\n",
       "        });\n",
       "    </script>\n"
      ],
      "text/plain": [
       "<pyecharts.render.display.HTML at 0x1f81f0a4048>"
      ]
     },
     "execution_count": 150,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 只需要在顶部声明 CurrentConfig.ONLINE_HOST 即可\n",
    "from pyecharts.globals import CurrentConfig, OnlineHostType\n",
    "# OnlineHostType.NOTEBOOK_HOST 默认值为 http://localhost:8888/nbextensions/assets/\n",
    "CurrentConfig.ONLINE_HOST = OnlineHostType.NOTEBOOK_HOST\n",
    "from pyecharts.charts import Bar\n",
    "import pandas as pd\n",
    "import pyecharts.options as opts\n",
    "df=df_result.groupby('高温',as_index=False).sum()\n",
    "x=df['高温']\n",
    "y=df['头等舱']\n",
    "y1=df['公务舱']\n",
    "y2=df['经济舱']\n",
    "bar = (\n",
    "    Bar(init_opts=opts.InitOpts(width='1000px',height='1000px',bg_color='white'))\n",
    "    .add_xaxis(list(x))\n",
    "    .add_yaxis('头等舱',list(y))\n",
    "    .add_yaxis('公务舱',list(y1))\n",
    "    .add_yaxis('经济舱',list(y2))\n",
    "    .set_global_opts(title_opts=opts.TitleOpts(\"高温对各舱数量影响分析图\"))\n",
    ")\n",
    "bar.render_notebook()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 2000x800 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "plt.rcParams['font.sans-serif']=['Simhei']\n",
    "plt.rcParams['axes.unicode_minus']=False\n",
    "df=df_result.groupby('低温',as_index=False).sum()\n",
    "x=df['低温']\n",
    "y=df['头等舱']\n",
    "y1=df['公务舱']\n",
    "y2=df['经济舱']\n",
    "width=0.3\n",
    "plt.figure(figsize=(20,8), dpi=100)\n",
    "plt.xlabel('低温温度(摄氏度)')\n",
    "plt.ylabel('各舱数量')\n",
    "plt.xticks(np.arange(-18,25))\n",
    "plt.title('低温对各舱数量影响分析图')\n",
    "plt.bar(x,y,width=width,color='#00FFFF') #青色\n",
    "plt.bar(x+width,y1,width=width,color='#FFA500') #橙色\n",
    "plt.bar(x+2*width,y2,width=width,color='pink') #粉色\n",
    "plt.legend(['头等舱','公务舱','经济舱'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 2000x800 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "plt.rcParams['font.sans-serif']=['Simhei']\n",
    "plt.rcParams['axes.unicode_minus']=False\n",
    "df=df_result.groupby('高温',as_index=False).sum()\n",
    "df1=df.iloc[:,2:6].sum(axis=1)\n",
    "df1_1=df.iloc[:,6:].sum(axis=1)\n",
    "rate_high=df1/df1_1\n",
    "# rate_high\n",
    "# print(np.array(df1))\n",
    "# df1=df['头等舱']+df['公务舱']+df['经济舱']+df['其他']\n",
    "# df1\n",
    "df2=df_result.groupby('低温',as_index=False).sum()\n",
    "df3=df2.iloc[:,2:6].sum(axis=1)\n",
    "df3_3=df2.iloc[:,6:].sum(axis=1)\n",
    "rate_low=df3/df3_3\n",
    "# # df2\n",
    "figure=plt.figure(figsize=(20,8),dpi=100)\n",
    "x=df['高温']\n",
    "plt.xticks(np.arange(-20,40))\n",
    "x1=df2['低温']\n",
    "y=rate_high\n",
    "y1=rate_low\n",
    "plt.xlabel('温度(摄氏度)',fontsize=16)\n",
    "plt.ylabel('上坐率',fontsize=16)\n",
    "plt.title('温度对上座率影响分析图')\n",
    "plt.plot(x,y,color='red')\n",
    "plt.plot(x1,y1,color='blue')\n",
    "plt.legend(['高温','低温'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- ## 4、数据分析建模并评估"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "<script>\n",
       "    require.config({\n",
       "        paths: {\n",
       "            'echarts':'http://localhost:8888/nbextensions/assets/echarts.min'\n",
       "        }\n",
       "    });\n",
       "</script>\n",
       "\n",
       "        <div id=\"7065cf122d5d49699522494e9fba8e2e\" style=\"width:900px; height:500px;\"></div>\n",
       "\n",
       "<script>\n",
       "        require(['echarts'], function(echarts) {\n",
       "                var chart_7065cf122d5d49699522494e9fba8e2e = echarts.init(\n",
       "                    document.getElementById('7065cf122d5d49699522494e9fba8e2e'), 'white', {renderer: 'canvas'});\n",
       "                var option_7065cf122d5d49699522494e9fba8e2e = {\n",
       "    \"backgroundColor\": \"white\",\n",
       "    \"animation\": true,\n",
       "    \"animationThreshold\": 2000,\n",
       "    \"animationDuration\": 1000,\n",
       "    \"animationEasing\": \"cubicOut\",\n",
       "    \"animationDelay\": 0,\n",
       "    \"animationDurationUpdate\": 300,\n",
       "    \"animationEasingUpdate\": \"cubicOut\",\n",
       "    \"animationDelayUpdate\": 0,\n",
       "    \"color\": [\n",
       "        \"#c23531\",\n",
       "        \"#2f4554\",\n",
       "        \"#61a0a8\",\n",
       "        \"#d48265\",\n",
       "        \"#749f83\",\n",
       "        \"#ca8622\",\n",
       "        \"#bda29a\",\n",
       "        \"#6e7074\",\n",
       "        \"#546570\",\n",
       "        \"#c4ccd3\",\n",
       "        \"#f05b72\",\n",
       "        \"#ef5b9c\",\n",
       "        \"#f47920\",\n",
       "        \"#905a3d\",\n",
       "        \"#fab27b\",\n",
       "        \"#2a5caa\",\n",
       "        \"#444693\",\n",
       "        \"#726930\",\n",
       "        \"#b2d235\",\n",
       "        \"#6d8346\",\n",
       "        \"#ac6767\",\n",
       "        \"#1d953f\",\n",
       "        \"#6950a1\",\n",
       "        \"#918597\"\n",
       "    ],\n",
       "    \"series\": [\n",
       "        {\n",
       "            \"type\": \"bar\",\n",
       "            \"name\": \"\\u5546\\u5bb6A\",\n",
       "            \"legendHoverLink\": true,\n",
       "            \"data\": [\n",
       "                5,\n",
       "                20,\n",
       "                36,\n",
       "                10,\n",
       "                75,\n",
       "                90\n",
       "            ],\n",
       "            \"showBackground\": false,\n",
       "            \"barMinHeight\": 0,\n",
       "            \"barCategoryGap\": \"20%\",\n",
       "            \"barGap\": \"30%\",\n",
       "            \"large\": false,\n",
       "            \"largeThreshold\": 400,\n",
       "            \"seriesLayoutBy\": \"column\",\n",
       "            \"datasetIndex\": 0,\n",
       "            \"clip\": true,\n",
       "            \"zlevel\": 0,\n",
       "            \"z\": 2,\n",
       "            \"label\": {\n",
       "                \"show\": true,\n",
       "                \"position\": \"top\",\n",
       "                \"margin\": 8\n",
       "            }\n",
       "        }\n",
       "    ],\n",
       "    \"legend\": [\n",
       "        {\n",
       "            \"data\": [\n",
       "                \"\\u5546\\u5bb6A\"\n",
       "            ],\n",
       "            \"selected\": {\n",
       "                \"\\u5546\\u5bb6A\": true\n",
       "            }\n",
       "        }\n",
       "    ],\n",
       "    \"tooltip\": {\n",
       "        \"show\": true,\n",
       "        \"trigger\": \"item\",\n",
       "        \"triggerOn\": \"mousemove|click\",\n",
       "        \"axisPointer\": {\n",
       "            \"type\": \"line\"\n",
       "        },\n",
       "        \"showContent\": true,\n",
       "        \"alwaysShowContent\": false,\n",
       "        \"showDelay\": 0,\n",
       "        \"hideDelay\": 100,\n",
       "        \"textStyle\": {\n",
       "            \"fontSize\": 14\n",
       "        },\n",
       "        \"borderWidth\": 0,\n",
       "        \"padding\": 5\n",
       "    },\n",
       "    \"xAxis\": [\n",
       "        {\n",
       "            \"show\": true,\n",
       "            \"scale\": false,\n",
       "            \"nameLocation\": \"end\",\n",
       "            \"nameGap\": 15,\n",
       "            \"gridIndex\": 0,\n",
       "            \"inverse\": false,\n",
       "            \"offset\": 0,\n",
       "            \"splitNumber\": 5,\n",
       "            \"minInterval\": 0,\n",
       "            \"splitLine\": {\n",
       "                \"show\": false,\n",
       "                \"lineStyle\": {\n",
       "                    \"show\": true,\n",
       "                    \"width\": 1,\n",
       "                    \"opacity\": 1,\n",
       "                    \"curveness\": 0,\n",
       "                    \"type\": \"solid\"\n",
       "                }\n",
       "            },\n",
       "            \"data\": [\n",
       "                \"\\u886c\\u886b\",\n",
       "                \"\\u7f8a\\u6bdb\\u886b\",\n",
       "                \"\\u96ea\\u7eba\\u886b\",\n",
       "                \"\\u88e4\\u5b50\",\n",
       "                \"\\u9ad8\\u8ddf\\u978b\",\n",
       "                \"\\u889c\\u5b50\"\n",
       "            ]\n",
       "        }\n",
       "    ],\n",
       "    \"yAxis\": [\n",
       "        {\n",
       "            \"show\": true,\n",
       "            \"scale\": false,\n",
       "            \"nameLocation\": \"end\",\n",
       "            \"nameGap\": 15,\n",
       "            \"gridIndex\": 0,\n",
       "            \"inverse\": false,\n",
       "            \"offset\": 0,\n",
       "            \"splitNumber\": 5,\n",
       "            \"minInterval\": 0,\n",
       "            \"splitLine\": {\n",
       "                \"show\": false,\n",
       "                \"lineStyle\": {\n",
       "                    \"show\": true,\n",
       "                    \"width\": 1,\n",
       "                    \"opacity\": 1,\n",
       "                    \"curveness\": 0,\n",
       "                    \"type\": \"solid\"\n",
       "                }\n",
       "            }\n",
       "        }\n",
       "    ]\n",
       "};\n",
       "                chart_7065cf122d5d49699522494e9fba8e2e.setOption(option_7065cf122d5d49699522494e9fba8e2e);\n",
       "        });\n",
       "    </script>\n"
      ],
      "text/plain": [
       "<pyecharts.render.display.HTML at 0x1f81fca2b08>"
      ]
     },
     "execution_count": 128,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from pyecharts.charts import Bar\n",
    "import pyecharts.options as opts \n",
    "bar = Bar(init_opts=opts.InitOpts(bg_color='white'))\n",
    "bar.add_xaxis([\"衬衫\", \"羊毛衫\", \"雪纺衫\", \"裤子\", \"高跟鞋\", \"袜子\"])\n",
    "bar.add_yaxis(\"商家A\", [5, 20, 36, 10, 75, 90])\n",
    "# render 会生成本地 HTML 文件，默认会在当前目录生成 render.html 文件\n",
    "# 也可以传入路径参数，如 bar.render(\"mycharts.html\")\n",
    "bar.render_notebook()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- ## 5、整理工程并部署：输入航班号、根据未来一周的天气（网上爬取的天气预报），预测并输出其上座率"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     True\n",
       "1    False\n",
       "2     True\n",
       "dtype: bool"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "from pandas import Series\n",
    "import pandas as pd\n",
    "from numpy import NaN\n",
    "series_obj = Series([None, 4, NaN])\n",
    "pd.isnull(series_obj)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
